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Variable screening and ranking using sampling-based sensitivity measures

Author

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  • Wu, Y-T.
  • Mohanty, Sitakanta

Abstract

This paper presents a methodology for screening insignificant random variables and ranking significant important random variables using sensitivity measures including two cumulative distribution function (CDF)-based and two mean-response based measures. The methodology features (1) using random samples to compute sensitivities and (2) using acceptance limits, derived from the test-of-hypothesis, to classify significant and insignificant random variables. Because no approximation is needed in either the form of the performance functions or the type of continuous distribution functions representing input variables, the sampling-based approach can handle highly nonlinear functions with non-normal variables. The main characteristics and effectiveness of the sampling-based sensitivity measures are investigated using both simple and complex examples. Because the number of samples needed does not depend on the number of variables, the methodology appears to be particularly suitable for problems with large, complex models that have large numbers of random variables but relatively few numbers of significant random variables.

Suggested Citation

  • Wu, Y-T. & Mohanty, Sitakanta, 2006. "Variable screening and ranking using sampling-based sensitivity measures," Reliability Engineering and System Safety, Elsevier, vol. 91(6), pages 634-647.
    • Handle: RePEc:eee:reensy:v:91:y:2006:i:6:p:634-647
    DOI: 10.1016/j.ress.2005.05.004
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    Citations

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    Cited by:

    1. Pengfei Wei & Chenghu Tang & Yuting Yang, 2019. "Structural reliability and reliability sensitivity analysis of extremely rare failure events by combining sampling and surrogate model methods," Journal of Risk and Reliability, , vol. 233(6), pages 943-957, December.
    2. Conceição António, Carlos & Hoffbauer, Luísa N., 2007. "Uncertainty analysis based on sensitivity applied to angle-ply composite structures," Reliability Engineering and System Safety, Elsevier, vol. 92(10), pages 1353-1362.
    3. Cheng, Lei & Lu, Zhenzhou & Zhang, Leigang, 2015. "Application of Rejection Sampling based methodology to variance based parametric sensitivity analysis," Reliability Engineering and System Safety, Elsevier, vol. 142(C), pages 9-18.
    4. Millwater, Harry & Singh, Gulshan & Cortina, Miguel, 2012. "Development of a localized probabilistic sensitivity method to determine random variable regional importance," Reliability Engineering and System Safety, Elsevier, vol. 107(C), pages 3-15.
    5. Tianxiao Zhang & Yimin Zhang, 2017. "A new model for reliability design and reliability sensitivity analysis of a hydraulic piston pump," Journal of Risk and Reliability, , vol. 231(1), pages 11-24, February.
    6. Wei, Pengfei & Liu, Fuchao & Tang, Chenghu, 2018. "Reliability and reliability-based importance analysis of structural systems using multiple response Gaussian process model," Reliability Engineering and System Safety, Elsevier, vol. 175(C), pages 183-195.

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